journal article·2016-02

Fatigue performance of a self-piercing rivet joint between aluminum and glass fiber reinforced thermoplastic composite

Amandine Gay, Fabien Lefebvre, Sébastien Bergamo, Frédéric Valiorgue, Pierre Chalandon, Philippe Michel, Philippe Bertrand

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Abstract

Abstract Lightweight structures are more and more widely used in the automotive industry due to the growing importance of environment regulations related to CO2 emissions. In this context of saving weight, composites offer an alternative to metals because they can achieve a better stiffness to weight ratio. A problematic becomes crucial: which process can be used to join efficiently composite and metallic parts? An innovative way would be the application of self-piercing riveting to thermoplastic composite–metal structure. Since the self-piercing riveting does not require a pre-drilled hole, the time process is short. Nevertheless, this process generates damage in the composite such as fiber cutting and delamination. The aim of this work is to evaluate the fatigue strength of a PA6.6-GF (Glass Fiber reinforced Polyamide 6.6)/Aluminum assembly joined by self-piercing riveting. Experimental results show that the SPR joint achieve high fatigue resistance on uniaxial shearing test. The joint strength at 2 · 106 cycles is more than half of the joint strength in static. Joint manufacturing process parameters like the rivet shape were investigated. Some fatigue tests were performed under severe environmental conditions like high temperature. This study shows that process parameters have less influences on the joint strength than the parameters that impact the composite resistance such as the composite type and the test temperature. The composite stresses evolution is useful for the failure modes analyses.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Advanced Welding Techniques Analysis · Fatigue and fracture mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (18)

Show source-provided references
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